{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Set current directory and work relative to it\n",
    "os.chdir('D:/Practical Time Series/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load the dataset into a pandas.DataFrame\n",
    "ibm_df = pd.read_csv('datasets/ibm-common-stock-closing-prices.csv')\n",
    "ibm_df.index = ibm_df['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataframe: (1009, 2)\n"
     ]
    }
   ],
   "source": [
    "#Let's find out the shape of the DataFrame\n",
    "print('Shape of the dataframe:', ibm_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>IBM common stock closing prices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-02</th>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>572.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-03</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>577.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-04</th>\n",
       "      <td>1962-01-04</td>\n",
       "      <td>571.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-05</th>\n",
       "      <td>1962-01-05</td>\n",
       "      <td>560.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-08</th>\n",
       "      <td>1962-01-08</td>\n",
       "      <td>549.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-09</th>\n",
       "      <td>1962-01-09</td>\n",
       "      <td>556.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-10</th>\n",
       "      <td>1962-01-10</td>\n",
       "      <td>557.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-11</th>\n",
       "      <td>1962-01-11</td>\n",
       "      <td>563.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-12</th>\n",
       "      <td>1962-01-12</td>\n",
       "      <td>564.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-15</th>\n",
       "      <td>1962-01-15</td>\n",
       "      <td>566.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  IBM common stock closing prices\n",
       "Date                                                   \n",
       "1962-01-02  1962-01-02                           572.00\n",
       "1962-01-03  1962-01-03                           577.00\n",
       "1962-01-04  1962-01-04                           571.25\n",
       "1962-01-05  1962-01-05                           560.00\n",
       "1962-01-08  1962-01-08                           549.50\n",
       "1962-01-09  1962-01-09                           556.00\n",
       "1962-01-10  1962-01-10                           557.00\n",
       "1962-01-11  1962-01-11                           563.00\n",
       "1962-01-12  1962-01-12                           564.00\n",
       "1962-01-15  1962-01-15                           566.50"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see the top 10 rows\n",
    "ibm_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-02</th>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>572.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-03</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>577.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-04</th>\n",
       "      <td>1962-01-04</td>\n",
       "      <td>571.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-05</th>\n",
       "      <td>1962-01-05</td>\n",
       "      <td>560.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-08</th>\n",
       "      <td>1962-01-08</td>\n",
       "      <td>549.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Close_Price\n",
       "Date                               \n",
       "1962-01-02  1962-01-02       572.00\n",
       "1962-01-03  1962-01-03       577.00\n",
       "1962-01-04  1962-01-04       571.25\n",
       "1962-01-05  1962-01-05       560.00\n",
       "1962-01-08  1962-01-08       549.50"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename the second column\n",
    "ibm_df.rename(columns={'IBM common stock closing prices': 'Close_Price'},\n",
    "              inplace=True)\n",
    "ibm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of rows with missing values: 0\n"
     ]
    }
   ],
   "source": [
    "#remove missing values\n",
    "missing = (pd.isnull(ibm_df['Date'])) & (pd.isnull(ibm_df['Close_Price']))\n",
    "print('No. of rows with missing values:', missing.sum())\n",
    "ibm_df = ibm_df.loc[~missing, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#To illustrate the idea of moving average we compute a weekly moving average taking\n",
    "#a window of 5 days instead of 7 days because trading happens only during the weekdays.\n",
    "ibm_df['5-Day Moving Avg'] = ibm_df['Close_Price'].rolling(5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UBo+15ThVzFdfWfn55zBqFKy/Ppx+Omy6aeKYlSutHD0annsOWreGI0P2+smTq7e9jpMt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29e4f38def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(2,1,1)\n",
    "ibm_df['Close_Price'].plot(ax=ax, color='b')\n",
    "ax.set_title('IBM Common Stock Close Prices during 1962-1965')\n",
    "ax = fig.add_subplot(2,1,2)\n",
    "ibm_df['5-Day Moving Avg'].plot(ax=ax, color='r')\n",
    "ax.set_title('5-day Moving Average')\n",
    "plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=2.0)\n",
    "plt.savefig('plots/ch2/B07887_02_14.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate the moving averages using 'rolling' and 'mean' functions\n",
    "MA2 = ibm_df['Close_Price'].rolling(window=2).mean()\n",
    "TwoXMA2 = MA2.rolling(window=2).mean()\n",
    "\n",
    "MA4 = ibm_df['Close_Price'].rolling(window=4).mean()\n",
    "TwoXMA4 = MA4.rolling(window=2).mean()\n",
    "\n",
    "MA3 = ibm_df['Close_Price'].rolling(window=3).mean()\n",
    "ThreeXMA3 = MA3.rolling(window=3).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Let's remove NaN from the above variables\n",
    "MA2 = MA2.ix[~pd.isnull(MA2)]\n",
    "TwoXMA2 = TwoXMA2.ix[~pd.isnull(TwoXMA2)]\n",
    "\n",
    "MA4 = MA4.ix[~pd.isnull(MA4)]\n",
    "TwoXMA4 = TwoXMA4.ix[~pd.isnull(TwoXMA4)]\n",
    "\n",
    "MA3 = MA3.ix[~pd.isnull(MA3)]\n",
    "ThreeXMA3 = TwoXMA4.ix[~pd.isnull(ThreeXMA3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tMjlmjIUF0jm7dtliw2OGnqDbDeN4KHwkudcthVtugdBQWL3a4gwPPmjrvydw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6d96f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(3, sharex=True)\n",
    "f.set_size_inches(5.5, 5.5)\n",
    "\n",
    "ibm_df['Close_Price'].iloc[:45].plot(color='b', linestyle = '-', ax=axarr[0])\n",
    "MA2.iloc[:45].plot(color='r', linestyle = '-', ax=axarr[0])\n",
    "TwoXMA2.iloc[:45].plot(color='r', linestyle = '--', ax=axarr[0])\n",
    "axarr[0].set_title('2 day MA & 2X2 day MA')\n",
    "\n",
    "ibm_df['Close_Price'].iloc[:45].plot(color='b', linestyle = '-', ax=axarr[1])\n",
    "MA4.iloc[:45].plot(color='g', linestyle = '-', ax=axarr[1])\n",
    "TwoXMA4.iloc[:45].plot(color='g', linestyle = '--', ax=axarr[1])\n",
    "axarr[1].set_title('4 day MA & 2X4 day MA')\n",
    "\n",
    "ibm_df['Close_Price'].iloc[:45].plot(color='b', linestyle = '-', ax=axarr[2])\n",
    "MA3.iloc[:45].plot(color='k', linestyle = '-', ax=axarr[2])\n",
    "ThreeXMA3.iloc[:45].plot(color='k', linestyle = '--', ax=axarr[2])\n",
    "axarr[2].set_title('3 day MA & 3X 3day MA')\n",
    "plt.savefig('plots/ch2/B07887_02_15.png', format='png', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
